Most middle and low-income countries developed their strategies against the COVID-19 spread based on measures designed by high-income countries. The literature shows that costs are higher in the first group, so computing the benefits of the measures is crucial to making decisions by policymakers. I use a difference-in-differences approach to estimate the effect of increasing the restrictive measures in Bogotá (Colombia) on the mortality rate. Specifically by implementing targeted lockdowns by locality. I find that there is no statistically significant effect even in the presence of spillovers. Also, I do not observe evidence of non-compliance drivers in the heterogeneity analysis. Using mobility data, I find that the main mechanism to explain these results is that people reduced mobility in Public Transport, which was guarded by authorities but did not reduce their mobility when they did not perceive that they were being watched. One of the possible interpretations of this result is given that there were other measures at the same time and a potential “wear effect”, the implementation of new more restricted measures has not a significant effect on the existing measures.
